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# /// script
# requires-python = ">=3.10"
# dependencies = [
# "trl>=0.12.0",
# "peft>=0.7.0",
# "transformers==4.51.0",
# "accelerate==1.6.0",
# "bitsandbytes>=0.41.0",
# "trackio",
# "datasets",
# ]
# ///
"""
Agent Zero SFT: zai-org/GLM-4.7-Flash (30B MoE)
QLoRA (4-bit) on a10g-large with pinned stable versions.
"""
import os
import torch
import trackio
from huggingface_hub import login
login(token=os.environ["HF_TOKEN"])
from datasets import load_dataset
from peft import LoraConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from trl import SFTTrainer, SFTConfig
print("Loading dataset...")
train_ds = load_dataset("wheattoast11/agent-zero-sft-v1", data_files="data/train.jsonl", split="train")
val_ds = load_dataset("wheattoast11/agent-zero-sft-v1", data_files="data/validation.jsonl", split="train")
print(f"Train: {len(train_ds)}, Val: {len(val_ds)}")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
)
offload_dir = "/tmp/offload"
os.makedirs(offload_dir, exist_ok=True)
print("Loading model in 4-bit...")
model = AutoModelForCausalLM.from_pretrained(
"zai-org/GLM-4.7-Flash",
quantization_config=bnb_config,
trust_remote_code=True,
device_map="auto",
max_memory={0: "21GiB", "cpu": "40GiB"},
offload_folder=offload_dir,
torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.7-Flash", trust_remote_code=True)
print("Model loaded.")
if hasattr(model, 'hf_device_map'):
devices = {}
for v in model.hf_device_map.values():
devices[str(v)] = devices.get(str(v), 0) + 1
print(f"Device distribution: {devices}")
config = SFTConfig(
output_dir="agent-zero-glm-4.7-v1",
push_to_hub=True,
hub_model_id="wheattoast11/agent-zero-glm-4.7-v1",
hub_strategy="every_save",
hub_private_repo=True,
num_train_epochs=2,
per_device_train_batch_size=1,
gradient_accumulation_steps=16,
learning_rate=1e-4,
bf16=True,
gradient_checkpointing=True,
logging_steps=10,
save_strategy="steps",
save_steps=50,
save_total_limit=2,
eval_strategy="steps",
eval_steps=50,
warmup_ratio=0.1,
lr_scheduler_type="cosine",
report_to="trackio",
project="agent-zero-finetune",
run_name="glm-4.7-flash-qlora-v1",
)
peft_config = LoraConfig(
r=16, lora_alpha=32, lora_dropout=0.05,
bias="none", task_type="CAUSAL_LM",
target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
)
print("Initializing trainer...")
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
train_dataset=train_ds,
eval_dataset=val_ds,
args=config,
peft_config=peft_config,
)
print("Starting training...")
trainer.train()
print("Pushing to Hub...")
trainer.push_to_hub()
trackio.finish()
print("Done!")
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